Innovative new technologies have expanded the opportunities for experiments searching for rare physical phenomenon. In physics, for example, these efforts are limited by the challenges of fast pattern recognition in triggering as particle detector hit densities increase with the high luminosities required to produce rare processes. Thus, many next-generation scientific experiments will be characterized by the collection of huge quantities of data, taken in rapid succession. Scientists will be tasked with unraveling the underlying physical processes. In most cases, large background hits will overwhelm the interesting detections related to the relevant physical phenomena. The quantity of data that can be stored for later analysis is limited. Therefore, real-time event selection is imperative to evaluating the relevant events while ignoring background detection.
A particular example of this is in high-energy physics, where scaling of current technologies is unlikely to satisfy the scientific needs of future projects. Investments in transformational new technologies need to be made, for example, in support of the silicon-based tracking trigger for the High Luminosity Large Hadron Collider (HL-LHC). The development of the silicon-based L1 tracking trigger system is critically important for the HL-LHC. The high occupancies anticipated at the HL-LHC and the low latencies required at L1 present a formidable set of challenges. Among these are the complex data dispatching, pattern recognition, and track fitting.
Data dispatching involves hits from many thousands of silicon modules that must be organized and delivered to an appropriate trigger. Due to the finite size of a beam's luminous region in the z direction and the finite curvature of charged particles in the magnetic field, some hits must be duplicated and sent to multiple triggers in an intelligent way. In addition, all of this must be done within a very short time (on the order of a micro-second). Thus, communication among processing elements in different trigger locations requires very high bandwidth and very low latency. In addition, extremely fast and effective pattern recognition and track fitting is also required. The bandwidth to bring all the incoming data from the massive silicon detector reaches to 100 Terabytes per second, and for every 25 nanosecond (i.e., beam crossing rate at 40 MHz) all the tracks from each beam crossing (with, on average, 140 interactions per crossing) will need to be fully reconstructed out of an ocean of background hits. Therefore, High Luminosity LHC requires advanced real time data processing technology.